Kusum L. Ailawadi, Donald R. Lehmann, & Scott A. Neslin ... · promotion. In contrast, the authors use Procter & Gamble's (P&G's) value pricing strategy as an opportunity to study

Kusum L. Ailawadi, Donald R. Lehmann, & Scott A. Neslin

Market Response to a Major PolicyChange in the Marketing Mix:

Learning from Procter & Gamble'sValue Pricing Strategy

Much research has focused on how consumers and competitors respond to short-term changes in advertising andpromotion. In contrast, the authors use Procter & Gamble's (P&G's) value pricing strategy as an opportunity tostudy consumer and competitor response to a major, sustained change in marketing-mix strategy. They compiledata across 24 categories in which P&G has a significant market share, covering the period from 1990 to 1996,during which P&G instituted major cuts in deals and coupons and substantial increases in advertising. The authorsestimate an econometric model to trace how consumers and competitors react to such changes. For the averagebrand, the authors find that deals and coupons increase market penetration and surprisingly have little impact oncustomer retention as measured by share-of-category requirements and category usage. For the average brandadvertising works primarily by increasing penetration, but its effect is weaker than that of promotion. The authorsfind that competitor response is related to how strongly the competitor's market share is affected by the change inmarketing mix and the competitor's own response and to structural factors such as market share position and multi-market contact. The net impact of these consumer and competitor responses is a decrease in market share for thecompany that institutes sustained decreases in promotion coupled with increases in advertising.

I n the past decade, researchers have made importantadvances in understanding hoth consumer and competi-tive response to advertising and promotion. Researchers

have quantified consumer response to promotion in terms ofhrand .switching, repeat purchase, stockpiling, and con-sumption (Ailawadi and Neslin 1998; Bell, Chiang, andPadmanabhan 1999; Gupta 1988) and investigated iheextent to which advertising attracts new users and retainsloyal customers (Deighton, Henderson, and Neslin 1994;Tellis 1988). In studying competitor response, researchers

Kusum L. Ailawadi is Associate Professor of Business Administration, andScott A. Neslin is Albert Wesley Frey Professor of Marketing, Amos TuckSchool ol Business Administration, Dartmouth College. Donald R.Lehmann is George E, Warren Professor of Business, Graduate School ofBusiness, Columbia University. The authors are grateful to Paul O'Connorand Jeannie Newton for their invaluable assistance with data compilationand to Pete Fader, Jeff Inman, Chris Moorman, and the University ofChicago Marketing Department for making some of the data used in thisarticle available. They also thank Frank Bass; Paul Farris; Karen Gedenk;Sunil Gupta; Kevin Keller; Punam Keller; Praveen Kopalle; Carl Mela;Vithala Rao; Robert Shoemaker; Frederick Webster Jr.; and participants atthe 1999 INFORMS Marketing Science Conference, the 1999 NortheastMarketing Consortium, and seminars at the University of North Carolina atChapel Hill, Boston University, and the Tuck School for helpful comments.Finally, the authors thank the four anonymous M reviewers for their sug-gestions. This research was supported by the Tuck Associates Program.The authors are listed in alphabetical order to reflect their equal contribu-tion to this research.

have assessed the role of marketing-mix elasticities(Leeflang and Wittink 1996; Putsis and Dhar 1998) andhegun to unravel the game theoretic structure of competi-tion (Cotterill, Putsis. and Dhar 2000; Kadiyali, Vilcassim,and Chintagunta 1999).

Although these studies have enhanced the understand-ing of how consumers and competitors respond to advertis-ing and promotion, they typically focus on response toshort-term changes in these marketing instruments. Rarelyhave researchers studied the effects of major policychanges. The purpose of this article is to examine marketresponse to a major and sustained change in advertising andpromotion policy. We use Procter and Gamble's (P&G's)value pricing strategy to examine consumer and competitiveresponse and to trace how this response ultimately affectsmarket share. Starting in 1991-92. P&G instituted majorreductions in promotion and increases in advertising in aneffort to reduce operating costs and strengthen brand loyalty(Shapiro 1992). This new policy ran counter to the generaltrend of promotion increases at the expense of advertisingthat prevailed in the packaged goods industry during thei980s and early 1990s (Donnelley Marketing Inc. 1995).

Analysis of market response to such a major policychange offers several advantages. First, it provides substan-tial variation in marketing-mix variables instead of week-to-week movement around a fairly stable level. This providesbetter estimates of the impact of marketing-mix variables.For example, Deighton, Henderson, and Neslin (1994) findthat advertising attracts consumers to the brand but does not

44 / Journal of Marketing, January 2001Journal of MarketingVol. 65 (January 2001), 44-61

increase purchase probabililies among current users. Theynole. however, that this finding may not be applicablebeyond the range of their data.

Second, the sustained policy change enables us to eval-uate the effectiveness of advertising and promotion in along-term setting. Studies based on short-term changes inadvertising and promotion often find that promotion has amuch stronger effect on market share than advertising does(e.g., Deighton, Henderson, and Neslin 1994; Sethuramanand Tellis 1991; Tellis 1988). However, Farris and Quelch(1987, p. 91) note that the effects of relatively minorchanges in advertising are hard to detect and recommendthai advertising experiments should be long enough formeasurable effects to occur.

Third, the policy change also enables us to study com-petitor response in a long-term setting. The reaction of com-petitors to marketing-mix changes by a market leaderdepends, at least to some extent, on consumer response elas-ticities (Gatignon, Anderson, and Helsen 1989; Leetlangand Wittink 1996). Thus, if consumer response to sustainedmarketing-mix changes differs from short-term response,competitor response is likely to be different.

Fourth, researchers have recently called for studyingresponses to fundamental policy changes that are of strate-gic importance to senior management, not just the tacticalchanges that are made in the short run (e.g., Abeele 1994).

Fifth, that P&G is the clear leader in this setting simplifiesthe analysis of competitive response in that we do not need todetermine first who, if anyone, is the leader (see, e.g., Kadiyali,Vilcassim, and Chintagunta 1999; Leeflang and Wittink 1996).

Finally, P&G's policy change was broad based. Our dataand analysis encompass 118 brands in 24 product categoriesin the packaged goods industry. This should make our resultsmore generalizable than those of other market response stud-ies that are based on one or two product-markets.

We use the sustained policy change enacted in P&G'svalue pricing move to investigate three major researchquestions:

•What are the roles of advertising and promotion in attractingand retaining customers?

•To what extent are competitor reactions determined by marketshare response elasticities and structural factors versus firm-specitlc strategy?

•How do consumer and competitive responses comhine todetermine the overall eltect ot a sustained change in advertis-ing and promotion on market share?

Our study is unique in integrating both consumer andcompetitor response to a substantial, sustained change inadvertising and promotion and in doing so across a largenumber of brands and product categories.

The article is organized as follows: We first discuss pre-vious work relevant to the central questions of this researchand present our conceptual model for analyzing them. Next,we describe the data and provide an overview of P&G'svalue pricing strategy. In the following sections, we presentresults for consumer and competitor response and show howthese responses combine to determine the overall impact onmarket share. Finally, we summarize our findings and dis-cuss implications for researchers and managers.

Theoretical Background andConceptual Model

Consumer ResponseA firm's advertising and promotion policy influences itsability to attract and retain customers by inducing more ofthem to(l)switch to the firm's brand. (2) repeat-purchase itmore often, or (3) consume larger quantities.

Brand switching. As described by Blattberg and Neslin(1990), promotions induce consumers to switch to a brandby improving short-term attitudes toward it, conditioningconsumers to respond to promotions, simplifying thepurchase decision, and reducing perceived risk. Thesetheories are supported by several empirical studies showingthat promotions result in a large brand-switching effect (e.g..Bell, Chiang, and Padmanabhan 1999; Gupta 1988).

Advertising can induce brand switching through eitherraising awareness or improving consumer attitudes (Vakrat-sas and Ambler 1999). Although there is ample evidencethat advertising influences awareness and attitudes, there isless evidence that it exerts a significant effect on brandswitching. Deighton, Henderson, and Neslin (1994) andLodish and colleagues (1995a, b) find that advertising helpssmall or new brands, presumably by attracting new users.

Repeat purchasing. Three theories point to a negativerelationship between promotion and repeat purchasing.First, self-percept ion and attribution theories suggest thatsteep price cuts encourage consumers to attribute theirpurchase of the brand to the promotion, not to underlyingpreferences (Dodson, Tybout. and Sternthal 1978). Second,behavioral learning theory indicates that promotions trainthe consumer to buy on deal rather than repeat-purchase thebrand (Rothschild 1987). Third, promotion can reduce theconsumer's reference price for the brand, causing "stickershock" on the next purchase (Winer 1986).

Theoretical support also exists for a positive effect ofpromotions. Promotions can be used to shape brand loyalty,thus increasing repeat rates (Rothschild and Gaidis 1981).Promotions can increase repeat rates in a competitive envi-ronment, because they preempt current users from takingadvantage of other brands' promotions and thereforeincrease purchase probabilities not only among new triersbut among current users as well.

Empirical research on promotion and repeat purchasinghas produced a range of fmdings, including a negative asso-ciation (Dodson, Tybout, and Sternthal 1978), no association(Ehrenberg, Hammond, and Goodhardt 1994), and both pos-itive and negative associations (Gedenk and Neslin 1999).

Theory regarding the role of advertising in repeat pur-chasing centers on attitudes and framing. Advertisingenhances beliefs, thereby improving attitudes and encourag-ing higher repeat rates. Framing theory suggests that adver-tising does not immediately persuade consumers but docspredispose them toward a favorable consumption experience.

Empirical evidence on the role of advertising in repeatpurchasing is mixed. Deighton (1984) provides evidence forframing, and Tellis (1988) finds that advertising increasesrepeat purchasing. Conversely, Deighton, Henderson, and

Procter & Gamble's Value Pricing Strategy / 45

Neslin (1994) find that advertising serves more to inducebrand switching than to reinforce repeat purchasing. Lodishand colleagues" (1995a, b) findings also suggest that adver-tising primarily influences brand switching rather thanrepeat purchasing (small and new brands have more cus-tomers to attract and fewer to retain).

Consumption. Promotion can increase the consumptionrate among a brand's users by inducing them to load up onthe brand and then consume it faster (Ailawadi and Neslin1998; Foikes, Martin, and Gupta 1993; Wansink andDeshpande 1994). However, promotion may also decreaseconsumption among a brand's users, because many of theseusers have bought only because of the promotion and maybuy smaller sizes because of perceived risk (Blattberg andNeslin 1990, p. 49). Wansink (1994) and Wansink and Ray(1996) provide theoretical and experimental evidence thatadvertising can induce higher category consumption bysuggesting new usage situations.

Moderators of consumer response. Researchers havefound that advertising and promotion elasticities differ bybrand as well as by product category. Probably the mostimportant brand characteristic found to influence marketshare elasticities is brand market share; High share brandshave weaker elasticities than low-share brands (Boiton 1989;Danaber and Brodie 1999; Ghosh, Neslin, and Shoemaker1983). Additional characteristics that bave been found toinfluence elasticities include promotional and advertisingactivity, stockpilabiiity, average purchase cycle, sales force,distribution, and advertising copy (Bell, Chiang, and Pad-manabhan 1999; Boiton 1989; Gatignon 1993; Ghosh, Neslin,and Shoemaker 1983; Kaul and Wittink 1995; Litvack,CaIan-tone, and Warshaw 1985; Narasimhan, Neslin, and Sen 1996).

Competitor Response

There are two streams of competitor response research. Themicro approach studies the nature of competitive interactionsin established markets using weekly or monthly data (e.g.,Kadiyali, Vilcassim, and Chintagunta 1999; Leetlang and Wit-tink 1996; Putsis and Dhar 1998). The macro approach focuseson the response of incumbent firms to new entrants in the mar-ket (e.g., Gatignon, Anderson, and Helsen 1989; Ramaswamy,Gatignon, and Reibstein 1994; Robinson 1988; Shankar1999). Both streams draw on the economics, industrial organi-zation, and/or strategy literature to identify three sets of factorsthat influence competitor response: (I) market share elastici-ties, (2) structural factors, and (3) finn-specific effects.

Market share response elasticities. Economic theoryposits that competitor response to a firm's change inadvertising and promotion is governed by cross-elasticities(how strongly the competitor's share is affected by thefirm's move) and self-elasticities (how easily the competitorcan recover lost share). For example, Leellang and Wittink(1996) show that competitors should react more strongly topreserve their market shares if their cross-elasticities arehigh. Although self- and cross-elasticities should determinewhether a competitor reacts, the empirical literature showsthat it is more difticult to predict their effect on how thecompetitor will react. For example, Gatignon, Anderson,and Helsen (1989), Putsis and Dhar (1998), and Shankar

(1999) show that firms compete more strongly witheffective weapons, that is, with variables for which theyhave strong self-elasticities. However, Brodie, Bonfrer, andCutler (1996) and Leeflang and Wittink (1996) find thatcompetitors often either overreact or underreact; Bell andCarpenter (1992) show that competitor response is differentdepending on the objectives sought; and Putsis and Dhar(1998) note that responses to cooperative versus competitivemoves can be very different.

Structural factors. Industrial organization theory sug-gests that market structure influences competitive interac-tion (Scherer and Ross 1990). Concentration of the mar-ket, market share position, and multimarket contact ofcompetitors with the initiating firm have been identified askey structural determinants of competitor response. Aswith elasticities, however, it is difficult to predict thedirection of their influence. For example, some researchersbelieve that multimarket contact increases competitiverivalry (Porter 1980), whereas others argue that it leads tomutual forbearance and dampens rivalry because the com-peting firms bave a high stake in many shared markets(Bernheim and Whinston 1990; Shankar 1999). Dominantcompetitors are expected to react differently from fringefirms, though it is not clear what the differences are (e.g.,Putsis and Dhar 1998; Shankar 1999; Spiller and Favaro1984). Market concentration is expected to increase coop-erative behavior, because monitoring is easier, signaling ismore likely to be perceived, and firms are less likely tocompete away high margins (e.g.. Quails 1974;Ramaswamy, Gatignon, and Reibstein 1994; Scherer andRoss 1990).

Firm-specific effects. The resource-based viewconceptualizes the firm as a unique entity with specific corecompetencies, leadership, culture, and resources thatdetermine its actions (Barney 1991; Chen 1996; Coliis1991; Wernerfelt 1984). Not surprisingly, the marketingliterature focuses on identifying and measuring themarketing resources of firms, such as brand equity, salesforce, advertising and general marketing expertise, andmarket orientation (e.g., Capron and Hulland 1999).Although market share position and marketing-mixelasticities may be imperfect surrogates for resources suchas brand equity and marketing expertise, other resourcesand competencies are unobserved or difficult to measureand are accounted for through a firm-specific effect (e.g.,Boulding and Staelin 1993).

Conceptual Framework

On the basis of this literature, we have developed the frame-work in Figure 1 to answer our research questions. The con-sumer response portion of this framework decomposes mar-ket share into three components and considers the effects ofown and competitive marketing mix on these components.These effects are moderated by brand and category charac-teristics. The competitive response portion posits that marketshare elasticities, structural factors, and firm-specific effectsdetermine competitor reaction. Finally, the net impact of apolicy change by a brand comes both directly, from con-sumer response to tbe brand's own policy change, and indi-

46 / Journal of Marketing, January 2001

FIGURE 1Conceptual Framework

•Brand Market Share Position•Category Characteristics

OwnPrice

AdvertisingDeals

Coupons

Market Share

PEN X SOR X USE

Competitors'Price

AdvertisingDeals

Coupons

•Market Share Elasticities•Structural Factors•Firm-Specific Effects

rectly, through competitors' reactions to the policy changeand consumers' response to the competitors' reactions.

Decomposition of market share. Market share, thecentral focus of our study, is the product of threecomponents: penetration (PEN), share-of-category-requirements (SOR), and category usage (USE). These threecomponents correspond to brand switching, repeatpurchasing, and consumption effects, respectively, as themeans by which advertising and promotion attract and retaincustomers. We define PEN a.s the proportion of categoryusers who purchase the brand at least once, SOR as theproportion of the brand's customers' category purchasesaccounted for by the brand, and USE as the averagecategory purchase volume of the brand's customerscompared with the average category purchases of allhouseholds buying the category. Market share equals PEN xSOR X USE.' For example, assume that P&G is purchased

'To examine this algebraically, lei

SB = unit brand sales,SQ = unit category sales,

MS = market share of the brand,Nc= number of households that buy the

category,Ng = number of households that buy the brand.

by 55% of all bar soap buyers. Buyers of P&G aresomewhat heavier users of bar soap. Their use of thecategory is 1.30 times that of the average category user.Only 49% of their bar soap purchases are accounted for byP&G, so they are not very loyal to P&G. Therefore, P&G'sshare in the bar soap category is .55 (PEN) x 1.30 (USE) x.49 (SOR), which is .3504, or 35.04% market share.

The component PEN is a measure of customer attrac-tion, because it reflects what proportion of category usersare attracted to the brand at least once; SOR is a measure ofcustomer retention, because it reflects how much of thebrand consumers buy after they have purchased it an initialtime. Practitioners use SOR as a measure of brand health,even loyalty (Bhattacharya et al. 1996; Hume 1992;

= average category unit sales per householdthat buys the category, and

= average category unit sales per householdthat buys the brand.

MS =PEN =

Then,

USE = Cg/Cc. andPEN X SOR X USE = (NB/NC) X ISBANB X Cg)] x

- | S B / ( N C X C C ) ] = (SB/SC) = MS.


Kristofferson and Lai 1996a, b). This measure reflects howwell the brand retains its customers and is particularly rele-vant in the context of P&G's value pricing strategy, becauseone of the stated goals of the strategy is to improve cus-tomer loyalty.

Effect of advertising and promotion. Our theoreticaldiscussion suggests that both advertising and promotionshould increase PEN by encouraging more consumers toswitch to the brand, though previous empirical researchsuggests that the advertising effect is much weaker than thepromotion effect. Advertising should have a positive effecton SOR, because it should enhance repeat purchasing.Promotion could have a negative or a positive effect onSOR. Advertising should have a positive effect on USE tothe extent that it suggests new product uses. Promotioncould have a negative or positive effect, depending onwhether it creates new usage occasions and increases theconsumption rate or brings in consumers who purchasesmaller quantities to mitigate perceived risk.

Moderators of advertising and promotion effects. Con-sumer response elasticities differ systematically from cate-gory to category and brand to brand. These cross-sectionaldifferences are not of direct interest for our research, but wemust control for them to obtain valid estimates on average.We allow market share position, stockpilability, purchasecycle, deal intensity, and advertising intensity to moderateself- and cross-elasticities in the consumer response model.Although we do not have data on all possible moderators(e.g., advertising copy), the consumer response researchcited previously suggests that the factors we includeexplain a significant portion of cross-sectional variation inelasticities.

Competitor response. Competitor response also differsfrom category to category and brand to hrand. It is moder-ated by market share elasticities and the structural and firm-specific factors suggested in the literature. Because marketshare elasticities are estimated in the consumer responsemodel, there is an important link between consumerresponse and competitor response. The structural factors aremarket concentration, market share position, and multimar-ket contact. Firm-specific factors over and above these arerepresented by dummy variables.-

MethodData

We study changes in the market between 1990 and 1996. In1991, P&G introduced its value pricing program and imple-mented it over multiple years (Kristofferson and Lai 1996a,b; Shapiro 1992), and we look at the market before, during,and after implementation of the strategy. Our data are pri-

do not include structural or firm-specific variables otherthan market .share position as moderators in the consumer responsemodel because they are not relevant to consumers. For example,consumers would not be expected to react differently to a givenbrand's advertising because that brand competes with anotherbrand in multiple markets.

marily from Information Resources Inc.'s (IRI's) annualMarketing Fact Book. We select 24 product categories inwhich P&G participates and compile data on price, promo-tion frequency and depth, market share, PEN, SOR, andUSE for several brands in each category. Whenever possi-ble, we include P&G, three of its largest competitors, and atleast one small competitor. The analysis is at the level of amanufacturer within a product category. That is, for a man-ufacturer with more than one brand within a product cate-gory, we combine the brands into one umbrella brand.- Thisresults in data on 118 brands across the 24 product cate-gories, though data for some brands are missing for one ormore of the seven years. The advertising data are compiledfrom the Leading National Advertisers publication of annualmedia advertising. All the moderators in the consumer andcompetitor response models are derived from these data,with the exception of category stockpilability, which weobtain from Narasimhan, Neslin. and Sen (1996). In Appen-dix A, we define all the variables, and in Appendix B, we listthe 24 product categories.

We note that our data reflect changes observed at the retaillevel, so we cannot separately measure the change in P&G'sstrategy toward the trade and the consequent change in strat-egy of the trade. Also, these data represent activity only inU.S. grocery stores and therefore do not speak to changes itiother countries or in other channels such as mass merchan-disers. However, the Marketing Fact Book covers a largenumber of packaged goods product categories and marketsand has been used to gain important insights by researcherssuch as Fader and Lodish (1990) and Lai and Padmanabhan(1995). Our compilation is particularly valuable because wecover a period of seven years, include most of the productcategories in which P&G plays a role, and augment the Mar-keting Fact Book data with media advertising data.

These data enable us to undertake a broad based pooledtime series, cross-sectional analysis. This is a well-estab-lished approach in the econometric literature (Hsiao 1986)that has produced several important articles in the marketingliterature (e.g.. Boulding, Lee. and Staelin 1994; BouldingandStaeiin 1990, 1993; Hagerty, Carman, and Russell 1988;Jacobson 1990; Jacobson and Aaker 1985). The advantageof this approach is that it enables broad and generalizableanalyses that span several brands, categories, or industries.The challenge is to control for cross-sectional differences sothat they are not confounded with longitudinal effects. Wemodel changes over time lo control for cross-sectional maineffects, and we incorporate moderators of both consumerand competitor response to control for cross-sectional inter-action effects.

Value Pricing: An Overview

In Table I, we present the average percent changes in price,advertising, and promotion made hy P&G and its competitorsbetween 1990 and 1996 across the 24 product categories. In

-•'This i.s consistent with the emphasis of packaged goods manu-facturers on category management and with the strategic emphasisof our research. It enables us to assess a firm's category perfor-mance as a function of its advertising and promotion policy in thatcategory.


Figure 2, we depict percent changes each year indexed totheir values in 1990, which serves as the base year.

Did P&G's publicized policy change affect retail activity?The answer to this is a clear yes. P&G increased advertisingexpenditures in the neighborhood of 20% over the sevenyears. It also reduced promotion activities: Deal frequencydeclined 15.7% and coupon frequency declined 54.3%. As aresult ofthe cuts In P&G's promotion, the net price paid hyconsumers increased by approximately 20%. Figure 2 alsoshows that these changes occurred gradually over severalyears, though price and deals had stabilized by 1995-96.

How did competitors respond? For advertising andcoupons, they followed P&G directionally but not com-pletely, and for deals, ihey did not follow P&G at all. Com-petitor advertising increased, but only by 6.2%; coupon fre-quency decreased, but only by 17.3%; and deal frequencyincreased by 12.6%. As a result, the net price paid by con-sumers for competitor brands increased, but only by 8.4%.Overall, then, competitors only partly went along withP&G's policy change. Table I shows the significant differ-ences in the reactions of different companies. Colgate andGillette were more competitive than Unilever.'* Both com-panies strongly increased advertising and promotion.Unilever, in contrast, cut coupons while instituting smallincreases in advertising and deals. We examine subse-quently how much of this difference in reaction across com-petitors is due to economic and structural factors and howmuch to firm-specific effects.

What was the net consequence of the strategy changeand competitive reaction for P&G's market share? Our datashow that during this period, P&G lost approximately 18%of its share on average across these 24 categories (approxi-mately five points per category). In line with changes in themarketing-mix variables, this share loss occurred graduallyover several years and stabilized somewhat during 1995-96.

Although this overview provides a broad picture ofP&G's initiative, our purpose is to gain an in-depth under-standing ofthe market response to the policy change. In the

refer to price decreases and advertising, deal, and couponincreases as competitive because these actions are intended to takeshare away from P&G. Analogously, we refer to price increasesand advertising, deal, and coupon cuts as cooperative. This termi-nology takes into accouni the inherent asymmetry noted byRamaswamy. Gatignon, and Reibstein (1994) and Putsis and Dhar(1998), who point out, for example, that following a price increasemay be cooperative but following a price decrease is not.

next two sections, we estimate econometric models of con-sumer and competitor response that enable us to examinenot just what happened but also why it happened. Specifi-cally, we are able to (1) quantify the individual effects ofadvertising and promotion on penetration, share of require-ments, and category usage; (2) account for differencesacross brands and categories; (3) determine the role of elas-ticities and structural and firm-specific factors in competi-tive response; and (4) attribute the total impact on P&G'smarket share to changes in each of its own marketing-mixvariables and to competitors' reactions.

FIGURE 2Overview of Value Pricing

P&G's Trends

•90 '91 '92 '93 '94 '95 '96

•Advertising —»-Deals —*—Coupons -•—Net price

150

125

100

75

25 •

Competitors'

-,_;---- y ^ i ^ J—"T" w^^

'90 ^91 '92 93

Year

—•—Advertising —•- Deals —*-

Trends

'94

- Coupons

"--^—^

•95 '96

- • - N e t price

TABLE 1Value Pricing: An Overview

Marketing Mix Variable P&G

Percent Change in Average from 1990 to 1996 for

All Competition Colgate Unilever Gillette

AdvertisingDealsCouponsNet price

+20.7%-15.7%-54.3%+20.4%

+6.2%+12.6%-17.3%

+8.4%

+67.3%+39.7%+24.0%

+2.5%

+9.0%+2.2%

-32.0%+ 11.5%

+68.8%+11.5%

+127.8%-7.7%


The Consumer Response ModelModel Specification

We model market share as a multiplicative function of mar-keting-mix variables, similar to Wittink and colleagues(1987) and Leeflang and Wittink (1996). Specifically,

(I) Share,,, = e«( Pricef '-

where

, = market share of brand i in category c in year trelative to 1990,

i = net price paid per unit volume for brand i incategory c in year t relative to 1990,

= advertising (in tens of millions of dollars) forbrand t in category c in year t relative to 1990,

= percentage of total sales of brand i In cate-gory c sold on deal in y.ear t relative to 1990,

= percentage of total sales of brand i in categoryc sold with manufacturer's coupons in year trelative to 1990,

= average (volume-weighted) Price of brand i'scompetitors in category c in year t relative to1990,

= average (volume-weighted) Advt of brand i'scompetitors in category c in year t relative to1990,

= average (volume-weighted) Deal of brand i'scompetitors in category c in year t relative to1990, and

= average (volume-weighted) Coup of brand i'scompetitors in category c in year t relative to1990,

Note that the dependent and independent variables in themodel are ratios with respect to the base year, 1990.'' Thisindexing is similar in spirit to Wittink and colleagues'(1987) and controls for brand-specific main effects. As dis-cussed previously, market share position and four categorycharacteristics moderate consumer response elasticities, sodifferent brands in different categories have their own elas-ticities, even though data for all categories and brands arepooled to estimate the model. This results in a process func-tion for elasticities (Gatignon 1993):

(2)

where

= Ko +

ic = elasticity for variable k in Equation 1 forbrand i of category c;

data are missing for 1990. we use 1991 as the base year.

ljc = ! if brand i in category c accounts for lessthan 5% of the sales of the top three hrandsin the category in 1990, 0 otherwise;''

jc = 1 if brand i in category c accounts for 5%to 40% of the sales of the top three brandsin the category in 1990, 0 otherwise;

= average percentage sold on deal in cate-gory c;

AvgAdvtgc = average advertising expenditure in cate-gory c;

AvgCycle^ = average length of purchase cycle for cate-gory c; and

= Stockpilability of category c.

We take logarithms of both sides and estimate the fol-lowing log-linear model using ordinary least squares(OLS):"

(3) !og(share;,,) - a + + ,.. +

We estimate this consumer response model using marketshare as well as the three components of market share. PEN,SOR, and USE. as dependent variables. Given the defini-tional identity relating market share to PEN. SOR, and USE,each coefficient estimate in the market share model equals thesum ofthe corresponding ct>efficient estimates from the mod-els for the three components (see Farris. Parry, and Ailawadi1992). As a result, the market share elasticity with respect toadvertising equals the penetration elasticity with resfiect toadvertising plus the SOR elasticity plus the USE elasticity.

ResultsIn Table 2, we summarize the fit of the consumer responsemodel for market share and its three components. AdjustedR^s are quite strong, showing that changes in the marketingmix are significantly related to changes in market share and

use a three-part classitlcation ol" market share position(small, middle, and high) to allow for nonlinear or nonmonolonicrelationships between market share and consumer response. Weinclude dummy variables for small and middle; high is ihe omittedcategory In our sample, the lowest quariileot market share relativeto the top three brands is approximately 7%. and the highest quar-tile is approximately 38%. We use slightly more extreme cutoffs of5% and 40% to separate the really small/fringe and really big/pow-erful brands from the majority in the middle.

^We correct for serial correction that may be caused by indexingto a base year and find that the estimated elasticities are not sub-staniively different from those obtained using OLS, We thankVithala Rao tor bringing the serial correlalion issue to our atten-tion. We also estimate the consumer response model using laggedvalues of the independent variables as instruments to control forpossible simultaneity and again find few substantive differences inestimated elasticities. Therefore, we report the OLS results here torsimplicity.


TABLE 2Consumer Response Model: Summary (569 observations)

Dependent Variable

StatisticNumber of

Coefficients PEN USE SOR Share

Adjusted R2 for full modelF-statistic for full modelF-statistic for category characteristicsF-statistic for brand market share position

56563216

.4479.19"4.31'9.51*

.3546.55"2.97*5.59*

.4308.64'6.73*6.05'

.4358.82'3.48'

10.81'

.01.

its components. Furthermore, the F-tests show that bothbrand and category characteristics have a significant influ-ence on consumer response.^

In Table 3. we present estimated elasticities for the aver-

age bratid, which suggest thai

•Price paid is negatively associated with market share, pri-marily through SOR and USE. That is. lower pricesenhance SOR and USE. Because our price variable is netprice paid, this effect includes the impact of promotionalprice cuts.

•Deals and coupons have similar share elasticities, exerted mostlythrough PEN. Both elasticities are fairly strong even though theyrepresent just tfie signaling aspect of promotions (the impact ofprice cuts themselves is included in the price variable).^

•The overall effect of promotion includes the signaling effectcaptured by the deal and coupon variables, plus the priceeffect captured by the price variable. Examining how theseeffects combine, we conclude that promotion clearly increases

^Although the multipficative model does not force market sharepredictions to lie between 0 and f 00%, there are no instances in oursample either of negative predicted market share or of marketshares u-ithin a category summing to more than 100%.

''We also estimate the m(Kfel using regular price, calculated asshown in Appendix A. As mighi be expected, price elasticities areweaker and deal and coupon elasticities are stronger in that model.We report the results on the basis of net price to be consistent withmost other scanner data-based studies (e.g., Bucklin and Lattinf99f; Chintagunta 1992. 1993; Fader and Lodish 1990; Mela,Gupta, and Lehmann 1997); also, overall model fit is better withnet price.

PEN, because the signs of the deal and coupon variables arepositive and the sign of the price variable is negative (mean-ing that price cuts increase penetration). For SOR and USE,the signs of the deal and coupon variables are negative, butthe signs of the price variable are also negative (meaning thatthe price cuts associated with deals would increase SOR andUSE). Even though the price coeftlcients are much larger thanthe deal coefficients, these balance out to mean that promo-tion has little effect on SOR and USE. This is because a fairlylarge percentage increase in deal frequency decreases netprice paid by only a small percentage.'^

•Advertising has a relatively weak relationship with marketshare, its biggest association is with PEN. but it has littleassociation with SOR."

'f*For example, consider a brand with a regular price of $2 and apromotion price of $ 1.40. Currently, 70% of purchases are at reg-ular price, and 30% are at the deal price. This yields a current netprice paid of $1.82. Consider an increase in deal frequency, so thatnow 40% of purchases will be at the deal price. This is a 33.3%increase in the deal variable. The average price paid will then be$1.76, a 3.3% decrease in the price variable. Assuming the averageelasticities as in Table 3. the new SOR compared with the currentSOR will be (.967)-2ftf' x (I.333)-O" = .9937, a marginal .63%decrease in SOR.

11 We estimate a model with current as well as lagged effects ofall advertising variables (main and interaction effects) to test forcarryovereffectsof advertising and cannot reject the null hypothe-sis that all lagged coefficients are zero. These results are consistentwith those of Lodish and colleagues {1995a), who find that ifadvertising does not have an effect in the first year, it does not haveany effect in subsequent years either.

TABLE 3Average Market Share Elasticities of Sample^

independent Variable PEN

Average Elasticities for

USE SOR Share

PriceAdvertisingDealsCouponsCompetitor priceCompetitor advertisingCompetitor dealsCompetitor coupons

-.065.044.162.133

-.027-.065-.106-.116

-.210.005

-.017-.001

.128-.003

.063

.035

-.266-.010-.053-.007

.665-.059-.081-.124

-.541.039.092.125.766

-.127-.124-.205

^Averaged across 118 brands in 24 categories.


•Cotnpetilive price cuts lead to lower share, mostly through anegative impact on SOR.

•Competitive advertising is associated with lower market sharethrough PEN and SOR. In other words, competitive advertis-ing makes it more difficult to attract and retain customers.

•Competitive deals and coupons are also associated with lowermarket share, largely through PEN and SOR.

Elasticities vary for different brands in different productcategories, as would be expected from the significant impactof brand and category cbaracteristics shown in Table 2. Forexample, the market share model estimates in Table 4 showthat heavily advertised categories are less price elastic, con-sistent with the role of advertising in product differentiation(e.g., Ghosh, Neslin, and Shoemaker 1983; Kaul and Wit-tink 1995). They are also less advertising elastic, consistentwith a saturation effect (e.g., Bolton 1989). Similarly, heav-ily promoted categories are less deal and coupon elastic,also consistent with a saturation effect. And categories withlong purchase cycles are less deal elastic, consistent with thefindings of Narasimhan, Neslin, and Sen (1996), but moreadvertising elastic. In terms of brand differences, smallbrands have the strongest advertising elasticities, consistentwith prior work by Deighton, Henderson, and Neslin (1994),

Lodish and colleagues (1995a), and Batra and colleagues(1995). They are also the most vulnerable to competitiveprice cuts and advertising, consistent with the literature onasymmetric effects (e.g., Blattberg and Wisniewski 1989).Large brands have the strongest promotion (both deal andcoupon) elasticities. Table 5 provides mean elasticities forsmall, mid-sized, and large brands in our sample.

Validation with Disaggregate Data

Although these findings are consistent with the theoreticaland empirical literature, they are based on aggregate data.To ensure that our results are not an artifact of category ortemporal aggregation, we validate our market share modelwith more disaggregate data. Specifically, we compile quar-terly market share, price, and promotion data over the1990-96 period for 26 brands in six product categories froma data set on the Chicago stores of the Dominick's grocerychain. The product categories are automatic dishwasherdetergent, dishwashing liquid, laundry detergent (powderand liquid combined), toilet tissue, toothpaste, and tooth-brushes. We obtain quarterly advertising data from LeadingNational Advertisers. Although the advertising data arenational, there is no reason to believe that changes in the

TABLE 4Estimates of Market Share Response Model

Marketing-MixVariable

Own priceOwn advertisingOwn dealOwn couponCompetitive priceCompetitive

advertisingCompetitive dealCompetitive coupon*p < .01.**p < .05.

MainEffect

.327-.289**2.099*

.373*"*-1.036

.357-.507-.473

SmallBrands

-.230.141*

-.236-.266"3.169"

-.428'.606*"

-.311*

Mid-sizedBrands

.033

.039-.270*""-.143"*-.186

-.026-.090

.108

Interaction With

CategoryAdvertising

.133***-.014*'"

.147"

.024"-.319"

.021

.005

.038

CategoryDeals

-.010.001

-.018*-.005*"

.021

-.002.001.002

Stockpiling

1.281"-.074*"-.011

.253*-1.042

-.065-.291-.382*

PurchaseCycle

-.012.003""

-.016'-.000

.014

-.004.004.003

TABLE 5Average Market Share Elasticities by Size of Brand

Independent Variable

Average Market Share Elasticities for

Small Brands

-.696.117.078

-.0013.220-.428

.377-.500

Mid-Sized Brands

-.481.024.018.132

-.061-.038-.312-.090

Large Brands

-.517-.017

.327

.266

.058-.001-.210-.166

Own priceOwn advertisingOwn dealsOwn couponsCompetitive priceCompetitive advertisingCompetitive dealsCompetitive coupons


Chicago market would be systematically different fromnationwide changes.

To allay concerns about aggregation over time, brands,and categories, we estimate our multiplicative market sharemodel separately for each brand in each category instead ofpooling data across brands and categories and using moder-ators. Thus, we obtain 26 sels of elasticity estimates. AsTable 6 shows, the pattern of elasticities from this disaggre-gate analysis is consistent with those obtained from theaggregate analysis. Overall, price elasticities are thestrongest, followed by promotion elasticities, whereasadvertising elasticities are quite weak, A comparison ofTahle 6 with Table 5 shows that differences in elasticitiesamong small, mid-sized, and large hrands are also similar tothe aggregate results. Small brands have the strongestadvertising elasticities and are the most vulnerable to com-petitive price cuts and advertising.'2 In summary, this dis-aggregate analysis corroborates the findings from our aggre-gate data, suggesting that the latter are not an artifact ofaggregation.

Competitive ReactionsModel SpecificationThe competitive response model consists of four equations,one for each of the four marketing-mix variables. Thedependent variable in each equation is the change in themarketing-mix variable of a given P&G competitor from thehase year to the current period,'^ As discussed previously,competitor response should depend on how strongly P&G'sstrategy change affects the market share of the competitor.Therefore, we model competitive marketing-mix reaction,Mmixch", as a function of Shreff, the predicted effect that

P&G's strategy change would have on the competitor's mar-ket share if the competitor does not change its strategy: i**

(4) = a , +

We model predicted share change on the basis of the fourmarketing-mix variables:

(5)

where

ti = change in marketing-mix variable k forhrand i in category c in year t relative to1990;

c, = predicted number of share points bywhich the market share of brand i in cat-egory c vi/ould change in year t relativeto 1990 as a result of P&G"s strategychange if there is no reaction;

o = market share of brand i in category c inthe base year 1990;

ic = cross-elasticity of hrand i in category cwith respect to variable k estimated inthe market share response model; and

icejct = percent change in the average (volume-weighted) Price of brand i's competitorsin category c from 1990 to year t,assuming P&G's actual change in price

i-Wc also estimate the basic niultiplicalive competitive interac-tion attraction model (Cooper and Nakanishi 1988) for each cate-gory and find that the estimated elasticities are generally consistentwith those from the multiplicative model.

i- We index competitive reaction to the base year (1990) for thesame reason as in the consumer response model. Furthermore, weconsider magnitude changes in a linear model rather than percentchanges in a log-hnear model because the fit of the former is sig-nificantly better for the advertising and coupon equations.

''•Shreff combines the impact of all four of P&G's marketing-mix variables. We do not model reaction to each of P&G'smarketing-mix variables separately because there is strong multi-collinearity among them. One option is to follow other researchersand model reactions only in kind (e,g,, model competitive pricechanges as a function of P&G's price alone, not its other market-ing-mix variables). We believe that combining the impact of allP&G's mix variables into a single Shreff variable is a better alter-native, because reactions may not occur only in kind (e,g.,Leeflang and Wittink 1992, 1996).

TABLE 6Market Share Elasticities: Disaggregate Data

Elasticity

Mean Value for

Entire Sample Small Brands Mid-Sized Brands Large Brands

Own priceOwn promotionOwn advertisingCompetitive priceCompetitive promotionCompetitive advertising

-1.119.139.035

1.700-.143-,048

-2,129.129.076

4,274.061

-.196

-,927.167.034

1.570-.236-.016

-.916.127.016.617

-.157-012


from year 1990 to year t and no changefor other competitors. Analogous defini-tions are used for the other marketing-mix variables.

In addition, a^ represents the change in competition's mar-keting-mix variable k even if Shreff is zero, which reflectsgeneral trends in the market and/or an overall cooperative orcompetitive stance by competitors; p^ indicates how com-petitors change variable k in response to Shreff A positivesign for k = Price means that competitors increase theirprices as P&G's move increases their share, A negative signmeans that competitors decrease their prices as P&G's moveincreases their share.

Because competitor response is moderated hy marketshare elasticities, structural factors, and firm-specificeffects, we allow for both intercept and slope shifts by thesefactors, i-" To control for other category characteristics thatmay moderate competitive response, we also group the cat-egories into five broad classes—health remedies, cleaners,personal care products, paper products, and food products—and include intercept and slope dummies for them:'^

16)

+ ekid'

whereself-elasticity of firm i in category c withrespect to marketing-mix variable k, esti-

ensure that we have enough observations to estimate acompany effect reliably, we include dummy variables only forcompanies that participate in at least five of the product categoriesin our study.

'^We thank an anonymous reviewer for suggesting that we addthese dummy variables.

mated from the market share responsemodel;

Cj. = concentration of category c, equal to mar-ket share of top three firms;

tj(. = number of product categories in whichfirm i competes with P&G;

Coljc = 1 if firm i in category c is Colgate Palmo-live, 0 otherwise;

Levi(. = 1 if firm i in category c is Unilever, 0otherwise;

Giljj. = i if firm I in category c is Gillette, 0 other-wise;

Healthy = I if category c is a health remedy, 0 other-wise;

Cleaner^ = I if category c is a cleaning product, 0otherwise;

Personalj. = I if category c is a personal care product, 0otherwise; and

Paper^ = I if category c is a paper product, 0 other-wise.

Results

The system of four competitive reaction equations is esti-mated from observations on P&G's competitors in all theproduct categories. We use seemingly unrelated regression,because the error terms across the four equations may becorrelated (Johnston and DiNardo 1997). The system-weighted R- for the four equations is .23,1'' In Table 7, wesummarize the results of tests to determine whether marketshare elasticities and structural, firm-specific, and categoryclass-specific factors have significant explanatory power.Each of these groups of factors is significant in all four com-petitive reaction equations, with the exception of the firm-specific effect in the advertising reaction equation. Thus,although a significant portion of competitor response is dueto self- and cross-elasticities and structural factors, a signif-icant portion is also firm-specific.

Figure 3 shows the response of the average competitoras a function of Shreff The length of the line represents therange of Shreff in the sample. The price, advertising, andcoupon intercepts being close to zero shows that competi-tors did not change these variables much during the periodof study if they were unaffected by P&G's policy change.

also estimate the competitive reaction model using one-year lags. There is no improvement in overall model fit, and noneofthe coefficient estimates changes significantly.

TABLE 7Competitive Reaction Model: Summary (376 observations)

F-Statistic for Impact of

Market share elasticitiesStructural factorsFirm-specificCategory class-specific

*p < .05,'*p< .10.***p< ,15.

14868

Price

4.55'4.34'3.71'6,26'

Competitive

Advertising

3.12'2.81*

.952.61'

Reaction Variable

Deals

2.54'5.59*3.21*6.32*

Coupons

1.44***5.79*5.47*2,94'


FIGURE 3Average Competitive Reactions

Price Change

7 - 6 - 5 - 4 - 3 - 2 - ^ 1 l l 1 2 3 4

Shrefi

Advertising Change

Shren

Deal Change

-40

Coupon Change

40

201

-40

1 2 3 4 5 6 7

Shreff

The deal intercept, however, is strongly positive, showingthat competitors generally increased deals during this periodeven if they were unaffected by P&G.

The nonzero slopes show that competitors generallyrespond depending on how strongly they are affected byP&G. Competitors are competitive on deals, increasingly sowhen they benefit from P&G's policy change. This is alsoreflected in price response: Competitors' prices decrease asthey benefit from P&G's policy change. Companies appearto use price and deals to take advantage of an opportunityfor increasing market share. Response on advertising andcoupons is quite different. Competitors cut advertising andcoupons if they benefit from P&G's policy change, perhapsusing the opportunity to save money.

Table 8 shows several interesting differences in compet-itive response due to structural and tlrm-specific factors. Forexample, competitors that have multimarket contact withP&G tend to follow P&G's lead on price and advertising

more than others when they benefit from P&G's move.Small competitors react in the opposite way, however. Theyreduce price and advertising more than others if they areaffected positively.'^ In addition, small brands are morelikely tban others to increase deals when they benefit fromP&G's move. These fmdings suggest that small brands areless likely to follow P&G's move, whereas firms with multi-market contacts are more likely to follow. Over and abovethe differences in response that can be accounted for by mar-ket share elasticities and structural factors, response differsby company, which lends support to the resource-basedview of the firm. For example, Gillette's reactions are lessrelated to how much it is affected by P&G's policy changethan Lever and Colgate's reactions, particularly on price andadvertising.

In summary, theory tells us that market share elasticities,structural factors, and firm-specific effects matter, and theydo. Theory does not predict the direction of influence ofthese factors, and we find that there are several interestingdifferences in their influence across marketing-mix vari-ables. These differences may be partly due to the initiatingfirm's move being cooperative on some variables (deals andcoupons) and competitive on others (advertising). Theremay be other issues at play, however, such as the costs andprofitability of various marketing-mix variables. Ourresearch points to the need for further theory development tobetter understand bow elasticities, structural factors, andfirm-specific factors influence competitive response.

Net Impact of a Sustained Changein Advertising and Promotion

PolicyImpact on Market ShareTo understand the impact of the value pricing strategy onP&G's market share more fully, we use P&G's elasticities,obtained from the consumer response model, together withthe changes made by P&G and the competition in each of thefour marketing-mix variables, to trace formally the overallimpact on the three components of P&G's market share—PEN. SOR, and USE. Table 9 suggests tbe following:

-On average, P&G suffered a predicted 16% loss in marketshare across ihe 24 categories in our study.'** The decrease inshare is due mainly to a predicted !7% decrease in penetration.SOR and USE are virtually unchanged. The hoped-for increasein SOR to offset the loss in penetration did not materialize.

•The loss in penetration is attributable to Pt&G's severe cuts incoupons and deals and the consequent increase in net price.

•The increase in nel price hurt SOR and USE. The deal andcoupon cuts did not increase SOR as might have beenexpected if cutting promotions strengthened customer loyalty.

"*This is partly attributable to a given share point change trans-lating to a much larger percentage of a small brand's market sharethan a large brand's market share.

•''The actual loss was 18%, so the mode! represents sharechanges pretty well.


TABLE 8Standardized Coefficient Estimates of Competitive Reaction Model

Independent Variable

Self-elasticityColgateLeverGilletteSmallMediumHealth remediesCleanersPersonal carePaperNumber of market contactsConcentrationShreffSelf-elasticity x ShreffColgate x ShreffLever x ShreffGillette x ShreffSnnall x ShreffMedium x ShreffHealth remedies x ShreffCleaners x ShreffPersonal care x ShreffPaper x ShreffNumber of contacts x ShreffConcentration x Shreff

Price Change

. 1 8 1 "

.012

.114-.081

.214*

.062

.323*-.004-.036-.033-.081-.080-.087-.405**-.597*

-1.114*-.008-.133*-.287*-.285*

.117-.612*

.0722.152*-.853

Dependent Variable^

Advertising Change

-.286*-.047-.141-.077

.208"*-.021

.201"- .179"

.056

.014

.261

.012-.052-.039-.443*- .587"-.003-.222'-.260*- .223*"-.063-.283

.0661.258*-.157

Deal Change

.261*

.346'

.231

.170*

.235*

.090-.438*-.081-.547*-.200*-.139-.0521.455*.081.155.028.011.194*

-.080-.216'*

.011-.279-.115-.246- .924"

Coupon Change

.167'"

.466*

.598*

.386*

.179*"

.238*-.203***

.201*'

.016

.050-.921"-.120***-.455

.214-.181-.139-.013-.003

.249"

.033-.196-.192-.138

.272

.273

^Magnitude change in competitor's marketing-mix variable from 1990 to current year.•p < .05." p < .10.• "p< .15 .

TABLE 9The Net Impact on P&G's Market Share

Variable

P&G priceP&G advertisingP&G dealsP&G couponsCompetitor priceCompetitor advertisingCompetitor dealsCompetitor couponsIntercept (e«)

Predicted Change Ratio

:hange Ratio(1996/1990)

1.2041.207.843.457

1.1001.0631.131.829—

PEN

Elasticity

-.129.026.178.161

-.161-.011-.145-.0801.015

.835

Impact

.9761.005.970.882.985.999.982

1.0151.015

USE

Elasticity

-.124-.001-.021

.008

.026-.012

.032

.0261.022

Impact

.9771.0001.004.994

1.003.999

1.004.995

1.022

.997

SOR

Elasticity Impact

-.295-.020

.012

.008

.439-.038-.087-.1011.030

1

.947

.996

.998

.9941.043.998.989

1.0191.030

.010

Share

Elasticity

-.548.005.169.177.304

-.061-.200-.1551.068

Impact

.9031.001.972.871

1.029.996.976

1.0301.068

.841

•The increase in deals by competitors exacerbated P&G's lossin penetration and had a negative impact on SOR. This nega-tive impact of competitor deal increases is just as strong as thenegative impact of P&G's own deal cuts.

•The competition's cuts in couponing and increases in netprice helped P&G's SOR and offset the decrements in SOR

resulting from P&G's increase in net price. But the competi-tion's coupon cuts did not help enough, and P&G still lostpenetration.

•Neither P&G's own advertising spending increases nor thecompetition's smaller increases had much effect, because bothself- and cross-advertising elasticities are quite weak.


Profit ImplicationsWe use our findings, along with some assumptions, to exam-ine the potential profit impact of value pricing. Our goal isnot to evaluate P&G's strategy but to explore consequencesother than market share that follow from a prolongedincrease in advertising and decrease in promotion. We use asimple margin calculation as in Hoch, Dreze, and Purk's(1994) study:

(7) Profit = Total Revenue - Total Variable Costs - Fixed Costs

= [{Manufacturer Price)(Unit Sales)]

- [(Variable Cost Per Unit)(Unit Sales)]

- [Fixed Costs).

First, the percent changes in price and market share inTables 1 and 9 show that total revenue should remain almostunchanged:-"

Gross

(8) Total [(I.2O4)(Price9o)l

= (.9849)(Total

Second, under the conservative scenario that variable costsper unit do not change, the 18.2% reduction in total unitssold leads to a corresponding reduction in total variable

(9) Total Variable CostSq^ = {.818)(Variable Cost per Unit)(Sales9o).

Thus, the gross margins in 1990 and 1996, respectively,would be

(10)

and

(II)

Gross Margingo =

- (Variable Cost per

Gross = (.9849)(Sales9o)(Price9o)

- (.818)(Variable Cost per Unit)(Sales9o).

Equation II shows that gross margin in 1996 would behigher than in 1990, especially if variable costs are large.

One question is whether this increase in gross marginwould be enough to cover the increased fixed costs of adver-tising. We obtained information about P&G's financialsfrom COMPUSTAT's annual data file. In 1990, P&G'sworldwide sales and advertising were about $24 billion and$2 billion, respectively, and the United States accounted for62% of sales, that is, $15 billion. Furthermore, P&G's costof goods sold as a percentage of revenue was approximately57% in 1990. Thus, we can estimate variable cost per unit as.57 times the 1990 price, and gross margins in 1990 and1996 can be written as

assumes that total category sales in the market do notchange (a reasonable assumption for mature categories) and thepercentage change in retail selling price is equal to the percentagechange in P&G's manufacturer selling price.

-'It is likely that variable costs per unit decreased because ofoperating efficiencies.

= (.9849)(Sales9o)(Price9o)

(12)

and

(13) Gross

= (l.2l)(Gross

Thus, P&G's U.S. gross margin can be expected to haveincreased by about $1.35 billion as a result of value pricing(.21 X .43 X $15 billion). Assuming that the ratio of U.S. toworldwide advertising is the same as the ratio of sales, U.S.advertising in 1990 would be approximately $1.24 billion(.62 X $2 billion), and the increase in fixed costs due to a 21%increase in advertising would be $.26 billion (.21 x $1.24 bil-lion). Thus, the increase in gross margin would be more thanenough to cover the increase in fixed advertising costs.According to our calculations, net profit would increase by$1.09 billion ($1.35 billion - $.26 billion).^^ In short, it isplausible that P&G gave up share points in return for profits.

ConclusionWe have used P&G's value pricing strategy to study theimpact of a major and sustained change in advertising andpromotion policy. We focus on the role of advertising andpromotion in attracting and retaining customers, the factorsthat infiuence competitive response, and the way theseeffects combine to determine overall market share impact.On tbe basis of previous theory and empirical work, wedevelop a framework for assessing this impact that inte-grates both consumer and competitive response. We test thisframework using data on 118 brands in 24 categories inwhich P&G competed over a seven-year period.

Summary of FindingsOur first research question relates to how advertising andpromotion work in attracting and retaining customers. Wefind tbe following:

•Promotion increases penetration and has little impact on cus-tomer retention as measured by SOR and USE.•Advertising appears to work primarily by increasing penetra-tion, but the effect is weaker than that of promotion. We findlittle evidence that adverti.sing increases SOR or USE amongthe average brand's existing users.

•Promotion has a stronger direct impact on share than adver-tising for the average packaged goods brand. This is a com-mon finding in both micro and macro short-term studies (e.g.,Boulding, Lee, and Staelin 1994; Tellis 1988) that has notbeen demonstrated previously in the case of a major and sus-tained policy change.

do not compare these figures with P&G's reported earn-ings, because our data cover only U.S. grocery channel sales forthese 24 categories and because we do not have information oncost cutting and other activities that P&G undertook during thisperiod.


Our second research question pertains to the relativeroles of market share response, structural factors, and firm-specific effects in determining competitor response. Wefind that

•Competitors' reactions to a sustained change in advertisingand promotion policy by a market leader vary with the degreeto which their market share is affected. Reactions also vary bystructural factors, such as the market share position of thecompetitor and the number of markets in which it competeswith the initiator of the policy change.

•Even after accounting for market share response elasticitiesand structural factors, there are still firm-specific effects incompetitor reactions.

•Competitors do not react the same vtay on all marketinginstruments. In this case, competitors tended to decreaseadvertising and coupons but used deals to gain market shareeven when they were benefiting from P&G's policy change.

Our third question is how consutner and competitorresponse combine to determine overall market share impact.We find that the net impact is a decrease in market share forthe initiating company, though it is plausible that its profitsincreased. The loss in penetration resulting from promotioncuts is not offset by increases in SOR or by competitivereactions. The effect of promotion on share is greater thanthat of advertising for established brands, though the reverseis true for small brands. The implication is that cuts in pro-motion, even if coupled with increases in advertising, willnot grow market share for the average established brand inmature consumer goods categories.

Limitations and Further ResearchAlthough these findings are both important and stimulating,our study has some limitations. First, we focus on a policychange that involves significant changes in advertising andpromotion. We do not have data on any changes P&G mayhave made in variables such as product assortment or newproduct development, so we cannot speak to their impact.Similarly, data limitations preclude the use of moderatorssuch as sales force, distribution, and advertising copy in ourconsumer response model (see Gatignon 1993). Althoughour purpose is not to study these moderators per se, includ-ing them might have generated additional insights. Nor dowe have measures of firm-specific factors such as cultureand management skill, though the statistical significance ofthe dummy variable surrogates we use for these factorsshould encourage further research in this area.

Second, we cannot make conclusive statements aboutthe impact of the strategy change on profitability in theabsence of data on P&G's manufacturer price and cost data.This would be an important area for further research if rele-vant data become available. Still, sales are a key objectivefor most companies, including P&G (see Brooker 1999;Fairclough 1999; Kristofferson and Lal 1996a, b), so webelieve that our analysis of market share is important.

Third, as noted previously, our analysis pertains to salesmade through the U.S. grocery channel, and alternativechannels such as mass and discount merchandisers havebecome more important in recent years. We note, however,that the shift to alternative channels is occurring for otherlarge competitors as well.

Fourth, we assume that P&G's adoption and consistentapplication of value pricing is exogenous; that is, P&Gdid not react to competition. With only seven time seriesdata points, we cannot investigate this issue. Trends inP&G's marketing mix over the seven-year period, how-ever, do not reveal any major pattern shifts or reversals. Ifa reversal in strategy occurs in the future, it would pro-vide another valuable opportunity to examine marketresponse.

Finally, our analysis is based on aggregate data. There Is atrade-off between Ihe micro approach to examining individualbrands, categories, or markets and the broad-based, strategicview provided by our analysis and others like it. We believethat both approaches contribute to understanding marketresponse and should be viewed as complementing each other.More important, many of our results are consistent with microstudies, and the product categories in which we are able toreplicate our analysis with disaggregate quarterly data alsocorroborate our findings. Still, it would be valuable to exam-ine differences between our conclusions and those based onmore disaggregate data, should those data be available. Suchdata would also enable researchers to study brand and cate-gory differences in market response in more detail and to studyinteractions between marketing-mix variables—for example,the effect of a brand's advertising and promotion on its priceelasticity (e.g., Gatignon 1993; Jedidi, Mela, and Gupta 1999).

Implications for Researchers and ManagersWhile acknowledging these limitations, we believe that ourresults are provocative and raise several important questionsand implications for researchers and managers:

What is the correspondence between SOR and brandloyalty? A sustained decrease in promotion and increase inadvertising should have enhanced P&G's SOR throughincreased brand loyalty. However, advertising did notimprove SOR. Furthermore, the net impact of cutting dealsand coupons and of the increase in net price paid was todecrease SOR. We believe this is due to the retention role ofprice promotions: In a competitive environment, an existinguser is more likely lo repurchase the brand when it is onpromotion than when it is not. But, even if promotions donot hurt SOR, underlying attitudinal loyalty could bediminishing. It is important for researchers to investigatethis further (e.g., Kopalle, Mela, and Marsh 1999), becauseSOR is a commonly used indicator of brand loyalty (e.g.,Bhattacharya 1997; Bhattacharya et al. 1996).

What does advertising do? We find that the direct effectof advertising on the components of market share is weak forall but small brands. This lack of effectiveness could beattributable to execution (e.g., Lodish et al. 1995a) or to theway advertising works for established brands in mature mar-kets. It is important to have a base level of advertising, buteven substantial increases over that base level may not gen-erate dramatic gains in market share for established brands.Our results are consistent with those of Tellis (1988),Deighton, Henderson, and Neslin (1994), and Batra and col-leagues (1995), even though we were working with a sub-stantial increase in advertising. In any event, more research isneeded on how and in what circumstances advertising works.


Market share elasticities and structural and firm-specific factors play a role in competitor response. Man-agers who try to anticipate competitor response must notonly know how their policy change will affect competitors'market share but also consider structural issues such as themarket share position of their competitors and in how manymarkets they compete. Furthermore, competitor-specificresources, capabilities, and culture also determine theresponse. Our findings strongly suggest the need for theoryIhat can predict not only the factors that determine compet-itive response but also the direction of their influence.

Competitors' response to policy changes is not uniformacross marketing instruments. The reasons for this need tobe sorted out. An obvious potential explanation would bedifferences in profitability across marketing instruments, butthere may also be some organizational factors at work. Forexample, it may be easier for managers to reallocate theirtotal marketing budgets than to change them drastically.

Researchers should make use of major policy changes.Dekimpe and Hanssens (1995) note that most markets arestationary, which precludes the finding of persistent effectsof marketing-mix variables. The substantial changes in mar-keting-mix variables as well as market share that accom-pany a major policy change provide a valuable opportunityto test for persistence. Jedidi, Mela, and Gupta (1999) do notfmd significant long-term effects of advertising on price orpromotion sensitivity. Perhaps such effects can be detectedwith more substantial variation in advertising. The P&Gvalue pricing policy is one example of a policy change, asare the Post cereal price rollback of 1998 and Marlboro'searlier price cutback. We conclude with a call for makinguse of these strategic policy changes to better understand theimpact of marketing strategy.

Appendix AVariables and Their Definitions

I. Variables obtained directly from IRI's Marketing FactBoot.A. Market share: percentage share of category volume.

. B. Household penetration: percentage of all householdsthat made at least one purchase of the brand.

C. SOR: among households that bought the brand, thepercentage of their total category purchases repre-sented by the brand.

D. Net price per unit volume: the average price paid perunit volume, reflecting all promotional activity.

E. Percent volume on deal: percentage of brand sales thatwere accompanied by some kind of deal, for example,display, feature, temporary price reduction (TPR).

F. Percent volume with manufacturer coupon: percent-age of brand sales that were made with a manufac-turer coupon.

G. Average percent price cut: the average percent sav-ings due to TPR or coupon redemption.

I!. New variables computed from IRI's Marketing FactBook data:

A. Shelf price: net price/[l - (percent on TPR orcoupon)(price cut)/IO,000].

B. PEN: brand penetration/category penetration.C. USE: average category purchase per brand

buyer/average category purchase per categorybuyer.

D. Market concentration: share of top three brands inthe market.

E. Number of market contacts: number of product cat-egories (of 24) in which a firm competes with P&G.

III. Media advertising spending from Leading NationalAdvertisers: In tens of millions of dollars. Minimum ofadvertising spending from remaining years used if com-pany is not found in Leading National Advertisers in agiven year.

IV. Category stockpilabiiity: Factor scores for each cate-gory provided by Narasimhan, Neslin, and Sen(1996).

Appendix BList of Product Categories

Health Remedies

1. Cold/allergy/sinus liquid2. Cold/allergy/sinus tablets3. Cough syrup

Cleaners

4. Dishwasher detergent5. Dishwashing liquid6. Dry bleach7. Liquid laundry detergent8. Powdered laundry detergent

Personal Care

9. Hair conditioner10. Hair spray11. Shampoo12. Bar soap13. Liquid soap14. Mouthwash15. Toothpaste16. Toothbrushes

Paper

17. Paper towels18. Diapers19. Facial tissue20. Toilet tissue

Food

21. Potato chips22. Frosting23. Brownie mix24. Shortening


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Kusum L. Ailawadi, Donald R. Lehmann, & Scott A. Neslin ... · promotion. In contrast, the authors use Procter & Gamble's (P&G's) value pricing strategy as an opportunity to study